Abstract
Classifying sequencing reads based on \(k\)-mer matches to a reference library is widely used in applications such as taxonomic profiling. Given the ever-increasing number of genomes publicly available, it is increasingly impossible to keep all or a majority of their \(k\)-mers in memory. Thus, there is a growing need for methods for selecting a subset of \(k\)-mers while accounting for taxonomic relationships. We propose \(k\)-mer RANKer (KRANK), a method that uses a set of heuristics to efficiently and effectively select a size-constrained subset of \(k\)-mers from a diverse and imbalanced taxonomy that suffers biased sampling. Empirical evaluations demonstrate that a fraction of all \(k\)-mers in large reference libraries can achieve comparable accuracy to the full set.
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The tool is available under https://github.com/bo1929/KRANK and data are available under https://github.com/bo1929/shared.KRANK. The full paper is available at http://doi.org/10.1101/2024.02.12.580015.
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Şapcı, A.O.B., Mirarab, S. (2024). Memory-Bound and Taxonomy-Aware K-Mer Selection for Ultra-Large Reference Libraries. In: Ma, J. (eds) Research in Computational Molecular Biology. RECOMB 2024. Lecture Notes in Computer Science, vol 14758. Springer, Cham. https://doi.org/10.1007/978-1-0716-3989-4_26
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DOI: https://doi.org/10.1007/978-1-0716-3989-4_26
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